Couler
A unified Python interface for constructing and managing workflows across engines like Argo Workflows, Tekton Pipelines, and Apache Airflow.
What is Couler?
Couler is a system for unified machine learning workflow optimization in the cloud. It provides a single programming interface to define workflows, abstracting away the complexities of different underlying workflow engines like Argo Workflows, Tekton, and Airflow. This approach enhances developer productivity and enables advanced automation and optimization features such as autonomous workflow construction and automatic artifact caching.
Machine learning engineers and data scientists who need to orchestrate and optimize complex ML workflows across cloud environments, particularly those using or evaluating multiple workflow engines like Argo Workflows, Tekton, or Airflow.
Developers choose Couler for its unified, engine-agnostic interface that simplifies workflow programming and its built-in optimization features like automatic parallelism and hyperparameter tuning, which reduce manual effort and improve computational efficiency.
Overview
Unified Interface for Constructing and Managing Workflows on different workflow engines, such as Argo Workflows, Tekton Pipelines, and Apache Airflow.
Use Cases
Best For
- Defining machine learning workflows with a single interface that can target multiple orchestration backends like Argo Workflows, Tekton, and Airflow.
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